This package provides functionality for exploring and visualising estimation results obtained with the software package BayesX for structured additive regression. It also provides functions that allow to read, write and manipulate map objects that are required in spatial analyses performed with BayesX, a free software for estimating structured additive regression models ( bayesx).

This software is also peer reviewed by journal JSS.

References in zbMATH (referenced in 46 articles , 2 standard articles )

Showing results 1 to 20 of 46.
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  1. Benjamin Taylor and Barry Rowlingson: spatsurv: An R Package for Bayesian Inference with Spatial Survival Models (2017)
  2. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  3. Haiming Zhou, Timothy Hanson, Jiajia Zhang: spBayesSurv: Fitting Bayesian Spatial Survival Models Using R (2017) arXiv
  4. John V. Monaco, Malka Gorfine, Li Hsu: General Semiparametric Shared Frailty Model Estimation and Simulation with frailtySurv (2017) arXiv
  5. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  6. Klein, Nadja; Kneib, Thomas: Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach (2016)
  7. Simon Wood: Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv (2016)
  8. Choi, Taeryon; Woo, Yoonsung: A partially linear model using a Gaussian process prior (2015)
  9. Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015)
  10. Eilers, Paul H. C.; Marx, Brian D.; Durbán, Maria: Twenty years of P-splines (invited article) (2015)
  11. Klein, Nadja; Kneib, Thomas; Lang, Stefan; Sohn, Alexander: Bayesian structured additive distributional regression with an application to regional income inequality in Germany (2015)
  12. Lang, Stefan; Steiner, Winfried J.; Weber, Anett; Wechselberger, Peter: Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits (2015)
  13. Page, Garritt L.; Quintana, Fernando A.: Predictions based on the clustering of heterogeneous functions via shape and subject-specific covariates (2015)
  14. Sperlich, Stefan; Theler, Raoul: Modeling heterogeneity: a praise for varying-coefficient models in causal analysis (2015)
  15. Zhou, Haiming; Hanson, Timothy; Knapp, Roland: Marginal Bayesian nonparametric model for time to disease arrival of threatened Amphibian populations (2015)
  16. Benjamin Hofner, Andreas Mayr, Matthias Schmid: gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework (2014) arXiv
  17. Bosq, Denis; Ruiz-Medina, María D.: Bayesian estimation in a high dimensional parameter framework (2014)
  18. Duarte, Elisa; De Sousa, Bruno; Cadarso-Suarez, Carmen; Rodrigues, Vitor; Kneib, Thomas: Structured additive regression modeling of age of menarche and menopause in a breast cancer screening program (2014)
  19. Kim, Hea-Jung; Choi, Taeryon: On Bayesian estimation of regression models subject to uncertainty about functional constraints (2014)
  20. Takele, Kasahun; Taye, Ayele: Bayesian modelling of growth retardation among children under-five years old (2014)

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